EP3422200A1 - Verfahren und system zum handhaben von einem oder mehreren themen in einer rechnerumgebung - Google Patents

Verfahren und system zum handhaben von einem oder mehreren themen in einer rechnerumgebung Download PDF

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EP3422200A1
EP3422200A1 EP17209090.4A EP17209090A EP3422200A1 EP 3422200 A1 EP3422200 A1 EP 3422200A1 EP 17209090 A EP17209090 A EP 17209090A EP 3422200 A1 EP3422200 A1 EP 3422200A1
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Prior art keywords
issues
issue
computing environment
handling system
critical features
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English (en)
French (fr)
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Arthi Venkataraman
Ajay Anantha
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Wipro Ltd
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Wipro Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0766—Error or fault reporting or storing
    • G06F11/0769—Readable error formats, e.g. cross-platform generic formats, human understandable formats
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0766—Error or fault reporting or storing
    • G06F11/0778—Dumping, i.e. gathering error/state information after a fault for later diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/079—Root cause analysis, i.e. error or fault diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5061—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the interaction between service providers and their network customers, e.g. customer relationship management
    • H04L41/5074—Handling of user complaints or trouble tickets
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0706—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment
    • G06F11/0709—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/069—Management of faults, events, alarms or notifications using logs of notifications; Post-processing of notifications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence

Definitions

  • the present subject matter is related, in general, but not exclusively, to a method and system for handling one or more technical issues (such as errors and faults) that occur in a computing environment.
  • mapping of exact action scripts to each of the different problem statements is a manual, time consuming activity.
  • manual process of mapping the action scripts to the corresponding problem statements is a challenging task due to the distributed nature of knowledge across people involved in the issue resolution process, both in terms of expressing a problem and interpretation of a problem statement.
  • the problem statements of the issues need to be broken down independently, and an association of the problem statements with corresponding solutions must be built.
  • to manually create a complete map of all the assets in the organization, their issues, and to provide resolutions is a very time-consuming activity. In addition, whenever new issues arise, the mapping process must be repeated.
  • the method comprises identifying, by an issue handling system, a problem statement associated with the one or more issues from one or more tickets. Upon identifying the problem statement, the method comprises extracting system log information related to the one or more issues from a system logger associated with the computing environment. Further, one or more issue templates are generated by mapping the problem statement of each of the one or more issues with corresponding system log information. Furthermore, one or more critical features in each of the one or more issue templates are determined based on one or more Natural Language Processing (NLP) components and predetermined issue parameters.
  • NLP Natural Language Processing
  • one or more clusters of issues are created based on semantic similarity between the one or more critical features and a distance matrix associated with each of the one or more issues. Finally, a correlation map of each of the one or more clusters is created for handling the one or more issues.
  • the issue handling system comprises a processor and a memory.
  • the memory is communicatively coupled to the processor and stores processor-executable instructions, which, on execution, causes the processor to identify a problem statement associated with the one or more issues from one or more tickets.
  • the processor extracts system log information, related to the one or more issues, from a system logger associated with the computing environment.
  • the processor generates one or more issue templates by mapping the problem statement of each of the one or more issues with corresponding system log information.
  • the processor determines one or more critical features in each of the one or more issue templates based on one or more Natural Language Processing (NLP) components and predetermined issue parameters.
  • NLP Natural Language Processing
  • the processor Upon determining the one or more critical features, the processor creates one or more clusters of issues based on semantic similarity between the one or more critical features and a distance matrix associated with each of the one or more issues. Finally, the processor creates a correlation map of each of the one or more clusters for handling the one or more issues.
  • the present disclosure relates to a non-transitory computer readable medium including instructions stored thereon that when processed by an issue handling system performs operations comprising identifying a problem statement associated with the one or more issues from one or more tickets.
  • system log information related to the one or more issues are extracted from a system logger associated with the computing environment.
  • one or more issue templates are generated by mapping the problem statement of each of the one or more issues with corresponding system log information.
  • the one or more critical features in each of the one or more issue templates are determined based on one or more Natural Language Processing (NLP) components and predetermined issue parameters for creating one or more clusters of issues based on semantic similarity between the one or more critical features and a distance matrix associated with each of the one or more issues. Finally, a correlation map of each of the one or more clusters is created for handling the one or more issues.
  • NLP Natural Language Processing
  • the present disclosure relates to a method and an issue handling system for handling one or more issues in a computing environment.
  • the method includes automatically mapping action scripts corresponding to the one or more issues with each of one or more problem statements related to the one or more issues.
  • the action scripts may be set of instructions that are run in the computing environment while solving an issue raised.
  • Action scripts can also be the scripts that are executed for solving an issue.
  • the method includes automatically updating the mapping between the action scripts and the one or more problem statements whenever a new issue arises in the computing environment.
  • the present method is also capable of implementing the action scripts that are mapped for handling the one or more issues automatically, while considering the distributed nature of knowledge across different people involved in the issue resolution process, both in terms of expressing a problem and interpretation of its problem statement.
  • the method further includes classifying the problem statements with a sophisticated Natural Language Processing (NLP) pipeline, which would help in building strong clusters of similar issues and associated problem statements based on their similarity. Also, the method uses system log information of the action scripts for clustering the same and/or similar set of problem statements, and to extract critical features of the one or more issues. Further, the issue handling system of the present disclosure may be configured to continuously learn and adapt to automatically map the newer issues and corresponding problem statements.
  • NLP Natural Language Processing
  • the method and the issue handing system of the present disclosure facilitate automated mapping of an issue or its problem statement to an action script using natural language analysis of the one or more problem statements, action scripts and system log information. Establishing the automated mapping between the one or more issues, and their problem statements with the action scripts helps in eliminating manual intervention required for handling the one or more issues.
  • the issue handling system uses a virtual user interface to receive one or more problem statements from a user and to provide one or more issue resolution scripts corresponding to the one or more problem statements to the user.
  • the issue resolution scripts are a set of instructions and actions that may be used for resolving the one or more issues.
  • FIG. 1 shows an exemplary view of a computing environment 100 having one or more issues 103 to be handled in accordance with some embodiments of the present disclosure.
  • the computing environment 100 includes an issue handling system 101, and a system logger 106.
  • the issue handling system 101 may be configured to handle one or more issues 103 in the computing environment 100.
  • the one or more issues 103 may be associated with one or more assets and/or computing systems in the computing environment 100.
  • the assets may include hardware and/or software.
  • the one or more issues 103 may include, without limiting to, network connectivity issues, data back-up issues, system lag issues, peripheral device connectivity issues, and the like.
  • the one or more issues 103 in the computing environment 100 are notified and/or reported by the one or more assets in the form of one or more tickets.
  • the one or more tickets corresponding to the one or more issues 103 may be retrieved from a ticketing system (not shown in FIG. 1 ) associated with the computing environment 100.
  • the ticketing system may be a computer software package that monitors and maintains a list of each of the one or more issues 103 in the computing environment 100.
  • the one or more tickets may include various critical information related to each of the one or more issues 103 such as, identity of assets in which the one or more issues 103 have occurred, problem statements 104 of the one or more issues 103, possible rectification actions for rectifying the one or more issues 103 and the like.
  • the system logger 106 in the computing environment 100 may be associated with each of the one or more assets in the computing environment 100 to monitor and keep a track of various actions performed by each of the one or more assets, messages and data generated by each of the one or more assets, and status of each of the one or more assets in the form of system log information 107.
  • the system log information 107 may be continuously updated with respect to working instances of the one or more assets.
  • the issue handling system 101 may generate one or more issue templates 109 by mapping the problem statements 104 associated with the one or more issues 103 and the system log information 107 related to the one or more issues 103.
  • the one or more issue templates 109 may indicate a one-to-one match among the one or more problem statements 104 identified in the computing environment 100 with respect to the system log information 107 related to the one or more assets. Therefore, the one or more assets responsible for the one or more issues 103 in the computing environment 100 may be determined based on the one or more issue templates 109.
  • the issue handling system 101 may determine one or more critical features 110 in each of the one or more issue templates 109 by applying one or more Natural Language Processing (NLP) techniques on the one or more issue templates 109 based on predetermined issue parameters.
  • the one or more critical features 110 may be in the form of keywords that highlight an intent of the one or more problem statements 104.
  • the one or more critical features 110 may be automatically determined by analyzing each of the one or more problem statements 104 using the NLP components based on the predetermined issue parameters.
  • the predetermined issue parameters may include, without limiting to, semantic parameters, temporal parameters, spatial parameters, and contextual parameters related to each of the one or more issues 103.
  • the issue handling system 101 may create one or more clusters of issues 114 based on semantic similarity between the one or more critical features 110 and a distance matrix 112 associated with each of the one or more issues 103.
  • Each of the one or more clusters may include one or more critical features 110 that are semantically related.
  • the distance matrix 112 may indicate relevance of each of a first set of critical features relative to a second set of critical features, such that both the first set of critical features and the second set of critical features are a subset of the one or more critical features 110.
  • the issue handling system 101 may create a correlation map 116 of each of the one or more clusters based on the correlation among each of the one or more clusters. Whenever there is a new issue in the computing environment 100, the issue handling system 101 may determine the right cluster of issues to which the identified issue must belong, based on the correlation map 116. Thus, the correlation map 116 helps in automatically classifying the one or more problem statements 104 associated with one or more issues 103 in the computing environment 100.
  • FIG. 2 shows a detailed block diagram illustrating the issue handling system 101 for handling the one or more issues 103 in the computing environment 100 in accordance with some embodiments of the present disclosure.
  • the issue handling system 101 includes an I/O interface 201, a processor 203, and a memory 205.
  • the I/O interface 201 may be configured to receive one or more problem statements 104 associated with the one or more issues 103 from a user of the computing environment 100 through a user interface (not shown in FIG. 2 ) associated with the user. Also, the I/O interface 201 may be interfaced with the user interface for providing the correlation map to the user for handling the one or more issues 103.
  • the memory 205 may be communicatively coupled to the processor 203.
  • the processor 203 may be configured to perform one or more functions of the issue handling system 101 for handling the one or more issues 103 in the computing environment 100.
  • the issue handling system 101 may include data 207 and modules 209 for performing various operations in accordance with the embodiments of the present disclosure.
  • the data 207 may be stored within the memory 205 and may include, without limiting to, one or more problem statements 104, system log information 107, predetermined issue parameters 211, distance matrix 112 and other data 213.
  • the data 207 may be stored within the memory 205 in the form of various data structures. Additionally, the data 207 may be organized using data models, such as relational or hierarchical data models.
  • the other data 213 may store data, including temporary data and temporary files, generated by the modules 209 for performing the various functions of the issue handling system 101.
  • the one or more problem statements 104 associated with the one or more issues 103 may be identified from the one or more tickets retrieved from the ticketing system associated with the computing environment 100.
  • the one or more problem statements 104 may be directly received from the user through the user interface.
  • the problem statements 104 may be in the form of a sentence in natural language, which would clearly indicate nature of the one or more issues 103 in the computing environment 100.
  • the issue handling system 101 may process each of the one or more problem statements 104 using the NLP components for extracting one or more key features from the one or more problem statements 104. Subsequently, the extracted key features may be used for mapping the one or more problem statements 104 with the corresponding system log information 107.
  • the system log information 107 may be extracted from a system logger 106 in the computing environment 100.
  • the system logger 106 may be interfaced with each of the one or more assets in the computing environment 100 to monitor and keep a track of various actions performed by the one or more assets, messages and data generated by the one or more assets, and status of the one or more assets in the form of system log information 107.
  • the system log information 107 may be continuously updated with respect to working instances of the one or more assets.
  • the system log information 107 may include, without limiting to, at least one of details related to one or more actions causing the one or more issues 103, timestamp of one or more commands or action scripts run corresponding to the actions, state of one or more environmental variables during execution of the actions and the action scripts run in the computing environment 100.
  • the issue handling system 101 may dynamically extract a most recent copy of the system log information 107 for classifying the issue by mapping the problem statements 104 of the issue with the appropriate system log information 107.
  • the predetermined issue parameters 211 may be used for determining the one or more critical features 110 in each of the one or more issue templates 109. Initially, the issue handling system 101 may analyze the one or more issue templates 109 using the NLP components. Further, the one or more critical features 110 may be determined by segregating information in the one or more issue templates 109 based on the predetermined issue parameters 211.
  • the one or more predetermined issue parameters 211 may include, without limiting to, semantic parameters, temporal parameters, spatial parameters, and contextual parameters related to each of the one or more issues 103.
  • the semantic parameters may indicate one or more keywords and key phrases in description of the one or more problem statements 104.
  • the temporal parameters may indicate time in which an action script was executed and total time taken for completion of execution of the action script.
  • the spatial parameters may indicate type of machine or computing system being used, and details of memory locations in which the action scripts are stored.
  • the contextual parameters may indicate set of commands used during execution of the action scripts and other environmental parameters changed during the execution.
  • the distance matrix 112 may be used in creation of the one or more clusters of issues 114 based on semantic similarity between the one or more critical features 110.
  • the distance matrix 112 may indicate relevance of each of a first set of critical features relative to a second set of critical features.
  • the first set of critical features and the second set of critical features may be associated with the one or more critical features 110.
  • the data 207 may be processed by one or more modules 209 of the issue handling system 101.
  • the one or more modules 209 may be stored as a part of the processor 203.
  • the one or more modules 209 may be communicatively coupled to the processor 203 for performing one or more functions of the issue handling system 101.
  • the modules 209 may include, without limiting to, an issue template generation module 215, a critical features determination module 217, a cluster creation module 219, a correlation map creation module 221 and other modules 223.
  • module refers to an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.
  • ASIC application specific integrated circuit
  • the other modules 223 may be used to perform various miscellaneous functionalities of the issue handling system 101. It will be appreciated that such modules 209 may be represented as a single module or a combination of different modules.
  • the issue template generation module 215 may be responsible for generating the one or more issue templates 109 by mapping the problem statements 104 of each of the one or more issues 103 with the corresponding system log information 107.
  • the issue template generation module 215 uses the problem statements 104 associated with the one or more issues 103 and maps them with the system log information 107 related to the one or more issues 103 to generate the one or more issue templates 109.
  • the critical features determination module 217 may be responsible for determining one or more critical features 110 in each of the one or more issue templates 109 based on one or more Natural Language Processing (NLP) components and predetermined issue parameters 211.
  • NLP Natural Language Processing
  • each problem statement 104 associated with the one or more issues 103 may include a set of sequential action scripts information associated with them.
  • the one or more issue templates 109 may include a collection of problem statements 104 and the associated action scripts, along with values of each of the predetermined issue parameters 211. Following is an exemplary representation of the issue template corresponding to a problem statement 104 - "I want to install the drivers for the printer".
  • the problem statement 104 - "I want to install the drivers for the printer” may be directly received from the user through the user interface. Further, one or more key features and nature of the issue may be determined by analyzing description of the problem statement 104 using the NLP components.
  • the action scripts associated with the problem statement 104 may be retrieved in real-time from the system log information 107 which is stored in the system logger 106.
  • the parameter - "actionScriptsMatched" may be a part of the system log information 107, and indicates the actual issue which has caused a problem in the computing environment 100.
  • the critical features determination module 217 may determine the one or more critical features 110 in the issue template.
  • the critical features determination module 217 may determine the critical features 110 such as, 'install', 'driver', 'printer', which indicate that the determined issue is related to installation of drivers for printers.
  • the issue template may include additional system log information 107 such as, type of the computing system (Ex.: systems running of Windows operating system), name of the asset having the issues (Ex.: printer), and location details of the asset or the identified issue (Ex.: region of memory having address - 'T20GB 111').
  • the one or more extracted critical features 110 may be classified based on the predetermined parameters in order to build a less sparse value set. For example, the one or more critical features 110 may be classified based on temporal parameters as a long script, a short script, a medium script, and the like. Similarly, the one or more critical features 110 may be classified based on spatial parameters indicating the implementation aspects of the computing system, such as Windows server, Linux server, Local server, and the like.
  • the cluster creation module 219 may be responsible for creating the one or more clusters of issues 114 based on the semantic similarity between the one or more critical features 110 and the distance matrix 112 associated with each of the one or more issues 103.
  • the cluster creation module 219 may use the one or more critical features 110 determined by the critical features determination module 217 and quantify each of the one or more critical features 110 to create the one or more clusters of issues 114.
  • the one or more clusters of issues 114 may include descriptions of the problem statements 104 that are semantically, spatially, temporally, and contextually similar. Also, the one or more clusters of issues 114 may include set of action scripts and system log information 107 that are mapped with the one or more problem statements 104.
  • the correlation map creation module 221 may be responsible for creating the correlation map 116 for handling the one or more issues 103.
  • the correlation map 116 of each of the one or more clusters may include a network of connections across the one or more clusters of issues 114 that have similar problem statements 104.
  • one or more nodes, each node representing one of the one or more clusters of issues 114 in the network are created and represented as a map of each of the one or more clusters of issues 114.
  • the correlation map creation module 221 may include a custom distance matrix 112 to each of the one or more clusters of issues 114 to indicate the relative distance among each of the one or more clusters of issues 114 in the correlation map 116.
  • the issue handling system 101 may process the problem statement 104 to classify intent of the problem statement. Further, the relevant action descriptions are processed and the one or more critical features 110 are determined. Upon determining the one or more critical features 110, the one or more critical features 110 are further classified to identify a correct cluster to which the one or more critical features 110 belong. Further, a custom distance matrix 112 may be used to traverse the correlation map 116 generated to identify the corresponding issue resolution scripts and associated parameters to run the resolution scripts for the cluster of issues that has been matched.
  • the issue resolution scripts are a set of instructions and actions that may be used for resolving the one or more issues.
  • the user may be provided with details of the best matched cluster and the issue resolution scripts.
  • the user may be provided an option to select one of the one or more issue resolution scripts provided on the user interface, which are subsequently used to create the correlation map 116.
  • a self-learning module in the issue handling system 101 may be used for modifying the one or more clusters and the correlation map 116 based on feedback from the user on the best-match cluster provided to the user. Based on the user feedback, the self-learning module may increase coefficient of mapping between the description of the problem statements 104 and the corresponding issue resolution scripts. Consequently, the user feedbacks would enable creation of more refined clusters of issuers containing very similar problem statements 104 and accurate mapping of issue resolution scripts in the correlation map 116.
  • the one or more users may face one or more issues 103 with the one or more computing systems while working on the one or more computing systems.
  • the one or more users may search and/or post each of the one or more issues 103 to the issue handling system 101 through the user interface associated with the issue handling system 101.
  • the one or more users may input a problem statement 104 corresponding to the one or more issues 103, as shown in Table A below.
  • the issue handling system 101 Upon receiving the problem statements 104 of each of the one or more issues 103, the issue handling system 101 extracts system log information 107 related to the one or more issues 103 from a system logger 106 associated with each of the one or more computing systems. Further, the issue handling system 101 applies one or more NLP components on each of the problem statements 104 to identify one or more critical features 110 associated with the problem statements 104. The one or more critical features 110 are used to create one or more clusters of issues 114.
  • the one or more critical features 110 that may be considered for creating the clusters of issues 114 may include, an NLP intent class identifier feature, an NLP object relative frequency identifier, machine type or type of servers running on each of the one or more computing systems and type of scripts run on each of the one or more computing systems.
  • the intent class identifier features may include several types of actions performed by the user on the one or more computing systems, which are represented by variables such as, 'Action - 1', 'Action - 2', 'Problem', and 'Req'.
  • the object relative frequency may be a numeric value between 0 to 1.
  • machine/server type critical features may include several types of servers running on the one or more computing systems, which are represented by variables such as, 'Local', 'Server - 1', and 'Server - 2'.
  • nature and type of the scripts may be represented by variables such as 'Run', 'Check', and 'Verify'.
  • the one or more critical features 110 that may be used for creating the one or more clusters of issues 114 are not limited to the one or more critical features 110 indicated in Table A and Table B below, but may include additional set of critical features as well. Using the additional set of critical features while creating the one or more clusters of issues 114 would further enhance the relativity among the clusters of issues 114, thereby leading to better association between the problem statements 104 and the corresponding one or more clusters of issues 114.
  • the above listed critical features 110 are respectively represented as critical feature 110 1 to critical feature 110 4 in Table A, in which, the critical feature 110 1 and critical feature 110 2 are derived from the NLP components and critical feature 110 3 and the critical feature 110 4 are derived from the system log information 107.
  • the one or more critical features 110 considered for creating the one or more clusters 114 are not limited to critical feature 110 1 to critical feature 110 4 , but may include other critical features 110 based on numerous factors including type of the issues detected, nature of the computing environment 100 and/or the computing systems. Table A - List of Problem statements and associated critical features.
  • the issue handling system 101 creates one or more clusters 114 of the one or more issues 103.
  • the problem statements 104 that relate to one or more issues 103 with 'Network Connectivity' in the one or more computing systems may be grouped into a single cluster.
  • the one or more issues 103 that relate to a similar type of script - 'Run' script, and being run on a 'Local' server may be grouped to a single cluster 114 - 'Cluster 1'.
  • the issue handling system 101 classifies and groups each of the one or more issues 103 into one or more clusters 114.
  • the one or more clusters 114 to which each of the problem statements 104 belong are indicated in 'Cluster No.' 301 column in Table B below.
  • Table B Grouping of problem statements into one or more clusters Problem statement 104 110 1 Critical feature 1 (NLP: intent class) 110 2 Critical feature 2 (NLP: object relative frequency) 110 3 Critical feature 3 (System log: Machine type 110 4 Critical feature 4 (System log: Script type) 301 Cluster No. Printer configuration required on my laptop Action - 1 0.7 Local Run 1 Browser need to be updated or installed Action - 1 0.65 Local Run 1 Need to reset BH application password Req - 1 0.4 Server - 1 Verify 2 I want to install chrome browser Action - 2 0.6 Local Run 1 Enable network connection through VPN on my pc.
  • Action - 1 0.5 Local Check 2 Outlook not loading emails. Problem 0.05 Local Check 3 Unable to setup skype meetings Problem 0.1 Server - 1 Verify 2 Repeated account lockout issue. Problem 0.05 Server - 2 Check 3 Request new login credentials for SF portal for testing. Action - 2 0.15 Server - 1 Verify 2 Need to back-up my D-Drive. Action - 1 0.55 Server - 2 Run 1
  • the issue handling system 101 creates a correlation map 116 of each of the one or more clusters 114.
  • the correlation map 116 is created with the help of a custom distance matrix 112 that indicates relevance of the one or more critical features 110 associated with the one or more clusters 114.
  • the correlation map 116 is created based on inter-cluster relevance between each of the one or more clusters 114.
  • the inter-cluster relevance i.e. distance between each of the one or more clusters 114 i.e. the distance matrix 112
  • FIG. 3 indicates inter-cluster relevance between the three clusters, 'Cluster 1', 'Cluster 1', and 'Cluster 1', which represents the relevance among each of the clusters - Cluster 1 to 3.
  • the distance between 'Cluster 1' and 'Cluster 2' is 0.2 units, and the distance between the cluster 'Cluster 1' and 'Cluster 3' is 0.4 units.
  • the relevance between 'Cluster 1' and 'Cluster 2' is more than the relevance between 'Cluster 1' and 'Cluster 3'.
  • the issues 103 in 'Cluster 1' are closely related to the issues 103 belonging to 'Cluster 2', as compared to the issues 103 belonging to 'Cluster 3'.
  • the users may report the detected issue 103 or the problem statements 104 of the detected issue 103 to the issue handling system 101 through the user interface.
  • the issue handling system 101 may determine one or more critical features 110 of the detected issue 103 based on relevant system log information 103 and NLP analysis of the problem statements 104.
  • the one or more critical features 110 may be classified and grouped into clusters 114.
  • the already generated correlation map 116 may be traversed using the distance matrix 112 to identify one or more issue resolution scripts and associated parameters required for resolving the detected issue 103.
  • the user may be provided with details of the issue resolution scripts and associated parameters through the user interface, thereby facilitating the users to resolve the detected issue 103.
  • FIG. 4 shows a flowchart illustrating a method for handling one or more issues 103 in a computing environment 100 in accordance with some embodiments of the present disclosure.
  • the method 400 includes one or more blocks illustrating a method for handling the one or more issues 103 in the computing environment 100 using an issue handling system 101, for example the issue handling system 101 of FIG. 1 .
  • the method 400 may be described in the general context of computer executable instructions.
  • computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform specific functions or implement specific abstract data types.
  • method 400 comprises identifying, by issue handling system 101, a problem statement 104 associated with one or more issues 103 from one or more tickets.
  • the one or more tickets may be extracted from a ticketing system associated with the computing environment 100.
  • the ticketing system may be an issue tracking system (also referred as trouble ticket system, support ticket, request management or incident ticket system), which may manage and maintain lists of issues, as needed by an organization.
  • method 400 comprises extracting, by the issue handling system 101, system log information 107 related to the one or more issues 103 from a system logger 106 associated with the computing environment 100.
  • the system log information 107 may include, without limiting to, at least one of details related to an action causing the one or more issues 103, timestamp of one or more commands run corresponding to the action, state of one or more environmental variables during the execution of the action and the action scripts run in the computing environment 100.
  • method 400 comprises generating, by the issue handling system 101, one or more issue templates 109 by mapping the problem statement 104 of each of the one or more issues 103 with corresponding system log information 107.
  • each problem statement 104 would have a set of sequential action scripts information associated with them, and would be used for mapping with the corresponding one or more issues 103.
  • method 400 comprises determining, by the issue handling system 101, one or more critical features 110 in each of the one or more issue templates 109 based on one or more Natural Language Processing (NLP) components and predetermined issue parameters 211.
  • NLP Natural Language Processing
  • the one or more NLP components may include at least one of dependency graph analysis, syntax tree analysis, semantic role labelling and associated rules of the NLP.
  • the one or more predetermined issue parameters 211 may include, without limiting to, semantic parameters, temporal parameters, spatial parameters, and contextual parameters related to each of the one or more issues 103.
  • method 400 comprises creating, by the issue handling system 101, one or more clusters of issues 114 based on semantic similarity between the one or more critical features 110 and a distance matrix 112 associated with each of the one or more issues 103.
  • the distance matrix 112 may indicate relevance of each of a first set of critical features 110 relative to a second set of critical features 110.
  • the first set of critical features 110 and the second set of critical features 110 may be associated with the one or more critical features 110.
  • method 400 comprises creating, by the issue handling system 101, a correlation map 116 of each of the one or more clusters for handling the one or more issues 103.
  • the correlation map 116 of each of the one or more clusters may include a network of connections across the one or more clusters of issues 114 that have similar problem statements 104.
  • the issue handling system 101 may create a map of each of the one or more clusters of issues 114 using the network of connections.
  • the issue handling system 101 may include a custom distance matrix 112 to each of the one or more clusters of issues 114 to indicate the relative distance among each of the one or more clusters of issues 114 in the correlation map 116.
  • handling the one or more issues 103 further comprises receiving the one or more problem statements 104 related to the one or more issues 103 from a user associated with the computing environment 100 through a user interface associated with the issue handling system 101. Further, a best-match cluster may be identified among the one or more clusters for handling the one or more issues 103 based on similarity of the one or more problem statements 104 with the one or more critical features 110 in the one or more clusters. Finally, the issue handling system 101 may provide the identified best-match cluster to the user through the user interface. In an embodiment, the best-match cluster may include one or more issue resolution scripts corresponding to the one or more problem statements 104. In some embodiments, the one or more clusters may be modified based on user feedback on the best-match cluster.
  • FIG. 5 illustrates a block diagram of an exemplary computer system 500 for implementing embodiments consistent with the present disclosure.
  • the computer system 500 may be the issue handling system 101 which is used for handling one or more issues 103 in a computing environment 100.
  • the computer system 500 may include a central processing unit (“CPU” or "processor") 502.
  • the processor 502 may comprise at least one data processor for executing program components for executing user- or system-generated business processes.
  • a user may include a person, a person using a device in the computing environment 100, or such a device itself.
  • the processor 502 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.
  • bus integrated system
  • the processor 502 may be disposed in communication with one or more input/output (I/O) devices ( 511 and 512 ) via I/O interface 501.
  • the I/O interface 501 may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE) or the like), etc.
  • the computer system 500 may communicate with one or more I/O devices ( 511 and 512 ).
  • the I/O interface 501 may be used to connect to a user interface 516 associated with a user.
  • the user interface 516 may be used to receive one or more problem statements 104 related to the one or more issues 103 from a user associated with the computing environment 100. Further, the user interface 516 may be used to provide a best-match cluster 517 to the user.
  • the best-match cluster 517 may include one or more issue resolution scripts corresponding to the one or more problem statements 104. Using the one or more issue resolution scripts, the user may resolve the one or more issues 103 in the computing environment 100.
  • the network interface 503 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/ 100 / 100 0 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11 a/b/g/n/x, etc.
  • connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/ 100 / 100 0 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11 a/b/g/n/x, etc.
  • the processor 502 may be disposed in communication with a memory 505 (e.g., RAM 513, ROM 514, etc. as shown in FIG. 5 ) via a storage interface 504.
  • the storage interface 504 may connect to memory 505 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc.
  • the memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.
  • the memory 505 may store a collection of program or database components, including, without limitation, user/application 506, an operating system 507, a web browser 508, and the like.
  • computer system 500 may store user/application data 506, such as the data, variables, records, etc. as described in this invention.
  • databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.
  • the operating system 507 may facilitate resource management and operation of the computer system 500.
  • Examples of operating systems include, without limitation, Apple Macintosh OS X, UNIX, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, Net BSD, Open BSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, K-Ubuntu, etc.), International Business Machines (IBM) OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry Operating System (OS), or the like.
  • a user interface may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities.
  • GUIs may provide computer interaction interface elements on a display system operatively connected to the computer system 500, such as cursors, icons, check boxes, menus, windows, widgets, etc.
  • Graphical User Interfaces may be employed, including, without limitation, Apple Macintosh operating systems' Aqua, IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, web interface libraries (e.g., ActiveX, Java, JavaScript, AJAX, HTML, Adobe Flash, etc.), or the like.
  • a computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored.
  • a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein.
  • the term "computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.
  • the present disclosure discloses a method for handling one or more issues arising in one or more assets of a computing environment.
  • the method of the present disclosure facilitates automated mapping of an issue or its problem statement to an action script using natural language analysis of the one or more problem statements, action scripts and system log information, thereby eliminating manual intervention required for handling the one or more issues.
  • the method of the present disclosure is capable of self-learning and helps in automatically mapping a new issue with an appropriate problem statement and the action script.
  • the method of present disclosure uses a virtual user interface to receive one or more problem statements from a user and to provide one or more issue resolution scripts corresponding to the one or more problem statements to the user, thereby assisting the user in handling of the one or more issues.
  • the issue handling system disclosed in the present disclosure may be used by a system engineer or a software test engineer as a real-time assist tool for handling the one or more issues in the computing environment.
  • an embodiment means “one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8527811B2 (en) * 2010-09-13 2013-09-03 International Business Machines Corporation Problem record signature generation, classification and search in problem determination
US20140244816A1 (en) * 2013-02-28 2014-08-28 International Business Machines Corporation Recommending server management actions for information processing systems

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8527811B2 (en) * 2010-09-13 2013-09-03 International Business Machines Corporation Problem record signature generation, classification and search in problem determination
US20140244816A1 (en) * 2013-02-28 2014-08-28 International Business Machines Corporation Recommending server management actions for information processing systems

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
AMELIE MEDEM ET AL: "TroubleMiner: Mining network trouble tickets", INTEGRATED NETWORK MANAGEMENT-WORKSHOPS, 2009. IM '09. IFIP/IEEE INTERNATIONAL SYMPOSIUM ON, IEEE, PISCATAWAY, NJ, USA, June 2009 (2009-06-01), pages 113 - 119, XP031504383, ISBN: 978-1-4244-3923-2 *

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